Performance Comparison of Deep Learning Techniques in Naira Classification

TL;DR

This paper evaluates various deep learning models for classifying Nigerian Naira banknotes by denomination, with MobileNetV2 achieving the highest accuracy, demonstrating potential for practical financial applications.

Contribution

It presents a comprehensive comparison of deep learning architectures for Naira note classification, highlighting MobileNetV2's superior performance in accuracy and robustness.

Findings

MobileNetV2 achieved 90.75% training accuracy.

Validation accuracy reached 87.04%.

Models performed well across diverse conditions.

Abstract

The Naira is Nigeria's official currency in daily transactions. This study presents the deployment and evaluation of Deep Learning (DL) models to classify Currency Notes (Naira) by denomination. Using a diverse dataset of 1,808 images of Naira notes captured under different conditions, trained the models employing different architectures and got the highest accuracy with MobileNetV2, the model achieved a high accuracy rate of in training of 90.75% and validation accuracy of 87.04% in classification tasks and demonstrated substantial performance across various scenarios. This model holds significant potential for practical applications, including automated cash handling systems, sorting systems, and assistive technology for the visually impaired. The results demonstrate how the model could boost the Nigerian economy's security and efficiency of financial transactions.